{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from pycaret.clustering import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 文件load\n",
    "csv_file = '../../datasets/diabetes.csv'\n",
    "diabetes = pd.read_csv(csv_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setup Succesfully Completed!\n"
     ]
    },
    {
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       "                        <td id=\"T_b4f698ae_1336_11eb_805d_54e1adcc3bd5row11_col1\" class=\"data row11 col1\" >None</td>\n",
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       "                        <td id=\"T_b4f698ae_1336_11eb_805d_54e1adcc3bd5row14_col1\" class=\"data row14 col1\" >False</td>\n",
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       "                        <td id=\"T_b4f698ae_1336_11eb_805d_54e1adcc3bd5row15_col1\" class=\"data row15 col1\" >None</td>\n",
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       "                        <td id=\"T_b4f698ae_1336_11eb_805d_54e1adcc3bd5row16_col1\" class=\"data row16 col1\" >None</td>\n",
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       "                        <td id=\"T_b4f698ae_1336_11eb_805d_54e1adcc3bd5row18_col0\" class=\"data row18 col0\" >Combine Rare Levels </td>\n",
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       "                        <td id=\"T_b4f698ae_1336_11eb_805d_54e1adcc3bd5row19_col1\" class=\"data row19 col1\" >None</td>\n",
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       "                        <th id=\"T_b4f698ae_1336_11eb_805d_54e1adcc3bd5level0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
       "                        <td id=\"T_b4f698ae_1336_11eb_805d_54e1adcc3bd5row20_col0\" class=\"data row20 col0\" >Numeric Binning </td>\n",
       "                        <td id=\"T_b4f698ae_1336_11eb_805d_54e1adcc3bd5row20_col1\" class=\"data row20 col1\" >False</td>\n",
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       "                        <th id=\"T_b4f698ae_1336_11eb_805d_54e1adcc3bd5level0_row21\" class=\"row_heading level0 row21\" >21</th>\n",
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       "                        <td id=\"T_b4f698ae_1336_11eb_805d_54e1adcc3bd5row21_col1\" class=\"data row21 col1\" >False</td>\n",
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       "                        <th id=\"T_b4f698ae_1336_11eb_805d_54e1adcc3bd5level0_row23\" class=\"row_heading level0 row23\" >23</th>\n",
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   "source": [
    "clf = setup(data = diabetes)"
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      "Best Model: K-Modes Clustering | Number of Clusters : 4 | Accuracy : 0.7721\n"
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      "Best Model: K-Means Clustering | Number of Clusters : 0 | Accuracy : 0.7708\n"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tuned_kmodes_new = create_model('kmodes', num_clusters=4)\n",
    "plot_model(tuned_kmodes_new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                       Name  \\\n",
      "ID                                            \n",
      "kmeans                   K-Means Clustering   \n",
      "ap                     Affinity Propagation   \n",
      "meanshift             Mean shift Clustering   \n",
      "sc                      Spectral Clustering   \n",
      "hclust             Agglomerative Clustering   \n",
      "dbscan     Density-Based Spatial Clustering   \n",
      "optics                    OPTICS Clustering   \n",
      "birch                      Birch Clustering   \n",
      "kmodes                   K-Modes Clustering   \n",
      "\n",
      "                                         Reference  \n",
      "ID                                                  \n",
      "kmeans                      sklearn.cluster.KMeans  \n",
      "ap             sklearn.cluster.AffinityPropagation  \n",
      "meanshift                sklearn.cluster.MeanShift  \n",
      "sc              sklearn.cluster.SpectralClustering  \n",
      "hclust     sklearn.cluster.AgglomerativeClustering  \n",
      "dbscan                      sklearn.cluster.DBSCAN  \n",
      "optics                      sklearn.cluster.OPTICS  \n",
      "birch                        sklearn.cluster.Birch  \n",
      "kmodes                           git/nicodv/kmodes  \n"
     ]
    }
   ],
   "source": [
    "print(models())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "763    0\n",
      "764    0\n",
      "765    0\n",
      "766    1\n",
      "767    0\n",
      "Name: Class variable, dtype: int64\n",
      "     Class variable    Cluster\n",
      "763               0  Cluster 0\n",
      "764               0  Cluster 0\n",
      "765               0  Cluster 0\n",
      "766               1  Cluster 1\n",
      "767               0  Cluster 0\n"
     ]
    }
   ],
   "source": [
    "test_data = diabetes.iloc[-5:]\n",
    "print(test_data['Class variable'])\n",
    "\n",
    "predictions = predict_model(tuned_kmodes_new, data = test_data)\n",
    "print(predictions[predictions.columns[-2:]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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